For a definition, cross-sectional data are the data accumulated at the same time or relatively the same point in time, whereas, time-series data are the data gathered across certain time periods. Under statistical data analysis, cross-sectional and time-series data are important. (More to read: Steps for qualitative data analysis) Quantitative data are always in the form of numbers that intimate either how much or how many. Qualitative data are labels or names that are implemented to find a characteristic of each element, whereas The discrete data is distributed under a discrete distribution function, also termed as the probability mass function.Р”Вata can either be quantitative or qualitative. The continuous data is distributed under continuous distribution function, also known as the probability density function, and (Related blog: Types of data in statistics) the number of bulbs, the number of people in a group, etc. The discrete data can be counted and has a certain number of values, e.g. The continuous data cannot be counted and changes over time, e.g the intensity of light, the temperature of a room, etc. (Related blog: An Introduction to Probability Distribution)Р”Вata is of two types, continuous data and discrete data. The image below shows the classification of data-variables. Here, the variable is a characteristic, changing from one individual trait of a population to another trait. If the data has a singular variable then univariate statistical data analysis can be conducted including t-test for significance, z test, f test, ANOVA test- one way, etc.Р”Р„nd if the data has many variables then different multivariate techniques can be performed such as statistical data analysis, or discriminant statistical data analysis, etc. Significance of data under statistical data analysis,Р”Вata comprises variables which are univariate or multivariate, and extremely relying on the number of variables, the experts execute several statistical techniques. (Recommend blog: Top Business Intelligence Tools and Techniques in 2020) The basic goal of statistical data analysis is to identify trends, for example, in the retailing business, this method can be approached to uncover patterns in unstructured and semi-structured consumer data that can be used for making more powerful decisions for enhancing customer experience and progressing sales.Р”Р„part from that, statistical data analysis has various applications in the field of statistical analysis of market research, business intelligence(BI), data analytics in big data, machine learning and deep learning, and financial and economical analysis.

In the context of business applications, it is a very crucial technique for business intelligence organizations that need to operate with large data volumes. thorough quantitative research that attempts to quantify data and employs some sorts of statistical analysis.Р’В Here, quantitative data typically includes descriptive data like survey data and observational data. What are the types of Statistical Data AnalysisР”В¤ steps process of Statistical Data AnalysisР”В«eing a branch of science, Statistics incorporates data acquisition, data interpretation, and data validation, and statistical data analysis is the approach of conducting various statistical operations, i.e. Significance of data in Statistical Data Analysis Moving discussion a step further, we shall discuss РІР‚СљStatistics is the specific branch of science from where the professionalists bring distinct conclusion/interference under the same dataРІР‚Сњ From delving into the overpowering quantity of data to precisely interpret its complexity in order to provide insights for intense progress to organizations and businesses, all sorts of data and information is exploited at their entirety and this is where statistical data analysis has a significant part.

In the information era, data is no protracted scarce, on the other hand, it is irresistible. РІР‚СљThe number of people who think they understand statistics dangerously dwarfs those who actually do, and maths can cause fundamental problems when badly used.РІР‚СњРІР‚вЂў Rory Sutherland

Statistical data analysis methods pdf